Quantum machine learning has shown advantages in many ways compared to classical learning. In learning, a difficult problem is how learn model with high robustness and strong generalization ability from limited feature space. Combining multiple models as base learners, ensemble (EL) can effectively improve the accuracy, of final model. The key EL lies two aspects, performance learners choice co...

Aiming at the deficiencies of sparrow search algorithm (SSA), such as being easily disturbed by local optimal and deficient optimization accuracy, a multi-strategy with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in strategy pool are proposed: variable logarithmic spiral saltation learning enhances global capability, neighborhood-guided accelerates convergence, adap...

Accurate electricity consumption forecasting in the power grids ensures efficient generation and distribution of electricity. Keeping this mind, paper introduces a novel deep learning model, termed Gated-FCN, for short-term load forecasting. The key idea is to introduce an automated feature selection model includes eight-layered Fully Convolutional Network (FCN-8) which hand-crafted that requir...

Abstract Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. However, there is disagreement about which are the best ensemble characteristics to obtain a good performance: som...

As a powerful data analysis technique, clustering plays an important role in mining. Traditional hard uses one set with crisp boundary to represent cluster, which cannot solve the problem of inaccurate decision-making caused by information or insufficient data. In order this problem, three-way was presented show uncertainty dataset adding concept fringe region. paper, we present improved algori...

Combining different physical and / or statistical predictive algorithms for Nuclear Power Plant (NPP) components into an ensemble can improve the robustness and accuracy of the prediction. In this paper, an ensemble approach is proposed for prediction of time series data based on a modified Probabilistic Support Vector Regression (PSVR) algorithm. We propose a modified Radial Basis Function (RB...

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In t...

Ensemble models can achieve more accurate predictions than single learners. Selective ensembles further improve the predictions by selecting an informative subset of the full ensemble. We consider reinforcement learning ensembles, where the members are neural networks. In this context we study a new algorithm for ensemble subset selection in reinforcement learning scenarios. The aim of the prop...

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the...